No description available.

Artificial neural networks (ANNs) have been used in many fault detection and identification (FDI) applications due to their pattern recognition abilities. In this study, two ANNs, a supervised network based on Backpropagation (BP) learning and an unsupervised network based on Adaptive Resonance Theory (ART-2A), were tested for FDI on an automated assembly machine and compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches were used as cost-effective sensors to monitor the machine's operating condition. To test each method, sensor data were collected while the machine operated under normal conditions, as well as 10 fault conditions. Features were selected from the raw sensor data to create data sets for training and testing. The performance of the methods was evaluated with respect to their ability to detect and identify known, unknown and multiple faults. Their modelling and computational requirements were also considered as performance measures. Results showed that all three methods were able to achieve perfect classification with the test data sets; however, the BP method could not classify unknown or multiple faults. In all cases, the performance depended on careful tuning of each method’s parameters. The BP method required an ideal number of neurons in the hidden layer and good initialization. The ART-2A method required tuning of its classification parameter. The rule-based method required tuning of its thresholds. Although it was found that the rule-based system required more effort to set up, it was judged to be more useful when unknown or multiple faults were present. The ART-2A network created new outputs for these conditions, but it could not give any more information as to what the new fault was. By contrast, the rule-based method was able to generate symptoms that clearly identified the unknown and multiple fault conditions. Thus, the rule-based method was judged to be the best overall method for this type of application. It is recommended that future work examine the application of computer vision-based techniques to FDI with the assembly machine. The results from this study, using cost-effective sensors, could then be used as a performance benchmark for image-based sensors. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2014-05-16 17:21:13.676

Thesis (M.S.)--Oregon State University, 2009. / Printout. Includes bibliographical references (leaves 59-62). Also available on the World Wide Web.

Thesis (Ph. D.)--California Institute of Technology, 1954. / Includes bibliographical references (p. 141-147).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 79-83). Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 209-214). Also available via the Internet.

In the subsurface faults can act as both barriers and conduits for fluids or gases such as CO2, hydrocarbons, or water. It is often thought that faults in porous rocks such as sandstone are barriers to fluid flow. In this study we show that this is not always the case. In sandstones like the Cedar Mesa Sandstone it is very important to understand the relationships between this history of fault slip and fluid flow. Better understanding of how fluids migrate through faults and the damaged areas surrounding these faults has strong significance to the oil and gas industry. In this study we examine a group of faults and their surrounding damage zones near Hite, Utah. We analyze three of these small-scale faults in more detail. In doing so we give insights into how these faults and their damage zones can effect fluid migration as well as the porosity and permeability in the Cedar Mesa Sandstone. Whole rock geochemistry, X-ray diffraction mineralogy, permeability data, petrography, ultraviolet photography, and outcrop observations were used to gain insights into cross-cutting relationships, past fluid compositions, and fault characteristics. From the data that was collected from these faults we have begun to describe a series of structural and fluid flow events. This series allows us to say that small-scale faults and fractures are features by which fluids can migrate preferentially. In this series of events we isolate two separate phases of movement. The first phase of movement being has a component of shear in which the edges of the fractures are not moving directly apart. This event is accompanied by a fluid flow event the emplaced iron oxide in the fractures and the surrounding formation. The second event is a phase when the faults become reactivated by a stress that created open mode fractures. This second is accompanied by a fluid flow event that has high calcium content and emplaces calcite in the fractures. Throughout this study we give evidence to support this series of movement and fluid events.

No description available.

No description available.